Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Profile Reconstruction from Private Sketches
Authors: Hao Wu, Rasmus Pagh
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show how to speed up their LP-based technique from polynomial time to O(d + n log n), where d = |D|, and analyze the achievable error in the ℓ1, ℓ2 and ℓ∞ norms. In all cases the dependency of the error on d is O(1/d) we give an informationtheoretic lower bound showing that this dependence on d is asymptotically optimal among all private, updatable sketches for the profile reconstruction problem with a high-probability error guarantee. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Copenhagen, Denmark. |
| Pseudocode | Yes | Algorithm 1 Private Profile Generator A( r), Algorithm 2 Fast Inversion Afst-inv, Algorithm 3 Rounding Arnd, Algorithm 4 Protocol P, Algorithm 5 Iterated Adjustment |
| Open Source Code | No | The paper does not contain any statements about providing open-source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper is theoretical and defines terms like 'multiset of n items from D' and 'finite domain D' but does not specify any particular dataset or provide concrete access information for a publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe any experimental validation process, nor does it specify any dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications (e.g., CPU, GPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies with version numbers for experimental setup. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, including hyperparameters or system-level training settings. |